NFL Stats by Division

Audrey & Nathan

6/14/19

Introduction

Focus: “Competitiveness” across the NFL

Data

Identifying info:

Outcome variables:

Data

Offense / Defense Stats

Manipulation: Creating New Variables

Question 1: Divisions

Is either division becoming more competitive over time?

Are there clear dominant teams, or are victories spread out across teams?

library(cowplot)
## 
## Attaching package: 'cowplot'
## The following object is masked from 'package:ggthemes':
## 
##     theme_map
## The following object is masked from 'package:ggplot2':
## 
##     ggsave
library(magick)
## Linking to ImageMagick 6.9.9.39
## Enabled features: cairo, fontconfig, freetype, lcms, pango, rsvg, webp
## Disabled features: fftw, ghostscript, x11
top_teams_by_year %>%
    group_by(team, div_general) %>%
    summarise(n = n()) %>%
    mutate(n = factor(n, levels = 1:10)) %>%
    ggplot(aes(x = n)) +
        geom_bar(aes(fill = div_general)) +
        scale_fill_manual(drop = F, values = c("red", "blue")) +
        scale_x_discrete(drop=FALSE) +
        # facet_wrap(~div_general) +
        labs(title = "Finishes in the top 4 win/loss ratio from 2009-2018") +
        theme_bw() -> plt
plt

sv <- 0.09
jaguars <-  "http://loodibee.com/wp-content/uploads/nfl-jacksonville-jaguars-team-logo-2-768x768.png"
chargers <- "http://loodibee.com/wp-content/uploads/nfl-los-angeles-chargers-team-logo-2-768x768.png"
jets <- "http://loodibee.com/wp-content/uploads/nfl-new-york-jets-team-logo-768x768.png"
raiders <- "http://loodibee.com/wp-content/uploads/nfl-oakland-raiders-team-logo-768x768.png"
ravens <- "http://loodibee.com/wp-content/uploads/nfl-baltimore-ravens-team-logo-2-768x768.png"
bengals <- "http://loodibee.com/wp-content/uploads/nfl-cincinnati-bengals-team-logo-768x768.png"
texans <- "http://loodibee.com/wp-content/uploads/nfl-houston-texans-team-logo-2-768x768.png"

x <- seq(from = -0.405, to = 0.6, by = 0.079)

ggdraw() +
    draw_plot(plt) +
    draw_image(jaguars, x = x[1], y = 0.27, scale = sv) +
    draw_image(chargers, x = x[1], y = 0.13, scale = sv) +
    draw_image(jets, x = x[1], y = -0.01, scale = sv) +
    draw_image(raiders, x = x[1], y = -0.15, scale = sv) +
    draw_image(chargers, x = x[1], y = -0.29, scale = sv) +
    draw_image(ravens, x = x[2], y = -0.29, scale = sv) +
    draw_image(bengals, x = x[3], y = -0.15, scale = sv) +
    draw_image(texans, x = x[3], y = -0.29, scale = sv) +
    draw_image(texans, x = x[4], y = -0.29, scale = sv) +
    draw_image(texans, x = x[5], y = -0.29, scale = sv) +
    draw_image(texans, x = x[6], y = -0.29, scale = sv) +
    draw_image(texans, x = x[10], y = -0.29, scale = sv)

top_teams_by_year %>%
    group_by(team, div_general) %>%
    summarise(n = n()) %>%
    filter(div_general == "AFC") %>%
    arrange(n)
## # A tibble: 7 x 3
## # Groups:   team [7]
##   team  div_general     n
##   <chr> <chr>       <int>
## 1 CIN   AFC             1
## 2 IND   AFC             1
## 3 KC    AFC             1
## 4 LAC   AFC             1
## 5 PIT   AFC             1
## 6 DEN   AFC             4
## 7 NE    AFC             6

Takeaways - Divisions

Competitiveness over time

Competitiveness across divisions

Question 4: Consistency

Graphing consistency

Question 3: Passing vs Running

Can teams remain competitive with different passing/running strategies?

Does either passing or running dominate among the most competitive teams, or are play styles similar across all teams?

Observations: Pass vs Run

Question 3: Offense/Defense

Are offense and defense equally important for a team to be competitive?

What separates the good teams from the bad teams?

Question 4: Consistency

Graphing consistency

Zooming in:

Conclusions

Limitations

Future Questions